mongodb-schema-design

MongoDB Schema Design

Data modeling patterns and anti-patterns for MongoDB, maintained by MongoDB. Bad schema is the root cause of most MongoDB performance and cost issues—queries and indexes cannot fix a fundamentally wrong model.

When to Apply

Reference these guidelines when:

• Designing a new MongoDB schema from scratch
• Migrating from SQL/relational databases to MongoDB
• Reviewing existing data models for performance issues
• Troubleshooting slow queries or growing document sizes
• Deciding between embedding and referencing
• Modeling relationships (one-to-one, one-to-many, many-to-many)
• Implementing tree/hierarchical structures
• Seeing Atlas Schema Suggestions or Performance Advisor warnings
• Hitting the 16MB document limit
• Adding schema validation to existing collections

Quick Reference

1. Schema Anti-Patterns - 3 rules

• antipattern-unnecessary-collections - Splitting homogeneous data into multiple collections is often an anti-pattern; consult this reference to validate whether this is the case.
• antipattern-excessive-lookups - When encountering overly normalized collections that reference each other or frequent and possibly slow $lookup operations, consult this reference to validate whether this is problematic and how to fix it.
• antipattern-unnecessary-indexes - Consult this reference when indexes overlap or are not used by queries, to identify and remove unnecessary indexes that add overhead without benefit.

2. Schema Fundamentals - 4 rules

• fundamental-embed-vs-reference - Consult this reference for approaches to modeling different types of relationships (1:1, 1:few, 1:many, many:many, tree/hierarchical data) and how to decide between embedding and referencing based on access patterns.
• fundamental-document-model - Fundamentals of the document model. Consult this reference when migrating from SQL or other normalized data to a document database like MongoDB.
• fundamental-schema-validation - Consult this reference when creating new collections, or adding validation to existing collections, for example in response to finding inconsistent document structures or data quality issues.
• fundamental-document-size - Consult this reference when documents hit the hard 16MB limit, or when accesses are slower than expected as a result of large documents.